Résumé: Conventionally, the exposure regarding knowledge of the inter vehicle
link duration is a significant parameter in Vehicular Networks to estimate
the delay during the failure of a specific link during the transmission.
However, the mobility and dynamics of the nodes is considerably
higher in a smart city than on highways and thus could emerge a complex
random pattern for the investigation of the link duration, referring
all sorts of uncertain conditions. There are existing link duration estimation
models, which perform linear operations under linear relationships
without imprecise conditions. Anticipating, the requirement to tackle the
uncertain conditions in Vehicular Networks, this paper presents a hybrid
neural network-driven mobility prediction model. The proposed hybrid
neural network comprises a Fuzzy Constrained Boltzmann machine
(FCBM), which allows the random patterns of several vehicles in a single
time stamp to be learned. The several dynamic parameters, which may
make the contexts of Vehicular Networks uncertain, could be vehicle speed
at the moment of prediction, the number of leading vehicles, the average
speed of the leading vehicle, the distance to the subsequent intersection
of traffic roadways and the number of lanes in a road segment. In this
paper, a novel method of hybrid intelligence is initiated to tackle such
uncertainty. Here, the Fuzzy Constrained Boltzmann Machine (FCBM) is
a stochastic graph model that can learn joint probability distribution over
its visible units (say n) and hidden feature units (say m). It is evident that
there must be a prime driving parameter of the holistic network, which
will monitor the interconnection of weights and biases of the Vehicular
Network for all these features. The highlight of this paper is that the
prime driving parameter to control the learning process should be a fuzzy
number, as fuzzy logic is used to represent the vague and and uncertain
parameters. Therefore, if uncertainty exists due to the random patterns caused by vehicle mobility, the proposed Fuzzy Constrained Boltzmann Machine could remove the noise from the data representation. Thus, the proposed model will be able to predict robustly the mobility in VANET, referring any instance of link failure under Vehicular Network paradigm.

BibTeX

@inproceedings {

SBM17,

title

=

"{Mobility Prediction in Vehicular Networks: An Approach through Hybrid Neural Network under Uncertainty}",